Spoken dialogue grammar induction from crowdsourced data

Elisavet Palogiannidi, Ioannis Klasinas, Alexandros Potamianos, Elias Iosif

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

Abstract

We design and evaluate various crowdsourcing tasks for eliciting spoken dialogue data. Task design is based on an array of parameters that quantify the basic characteristics of the elicitation questions, e.g., how open-ended is a question. The crowdsourced data are used for and evaluated on the unsupervised induction of semantic classes for speech understanding grammars. We show that grammar induction performance is significantly affected by the crowdsourcing task parameters, e.g., paraphrasing tasks prime high lexical entrain-ment and result in poor corpus/grammar quality. The task parameters along with perplexity filters are used for corpus selection achieving grammar induction performance that is comparable to that of using in-domain spoken dialogue data.

Original languageEnglish
Title of host publication2014 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2014
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages3211-3215
Number of pages5
ISBN (Print)9781479928927
DOIs
Publication statusPublished - 2014
Externally publishedYes
Event2014 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2014 - Florence, Italy
Duration: 4 May 20149 May 2014

Publication series

NameICASSP, IEEE International Conference on Acoustics, Speech and Signal Processing - Proceedings
ISSN (Print)1520-6149

Other

Other2014 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2014
Country/TerritoryItaly
CityFlorence
Period4/05/149/05/14

Keywords

  • Crowdsourcing
  • Grammar Induction
  • Spoken Dialogue Systems

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